The traditional deliberative, or global, navigation pipeline is heavily
model based (in the sense that it requires models of the robot and the
world) and is at odds with end-to-end machine learning approaches. Several
ML-navigation based papers further conclude that it is best to fuse their
algorithms with higher level planners to overcome the short-term, greedy
nature of ML-navigation [1]}, [2]}.
One structural issue with doing so is that typical end-to-end systems are
often not meant to consider higher level planning actions, thus there is no
obvious procedural strategy for achieving the desired recommendation. An
exception being ML-strategies that explicitly incorporate the task
[2]}, or that learn motion primitives
[4]}. The latter can be concatenated with a higher
level planner. Additional work is needed though to align the motion
primitive control decisions with the global goal.
